Arindam Chaudhuri is currently working as Principal Data Scientist at the Samsung R & D Institute in Delhi, India. He has worked in industry, research, and academics in the domain of machine learning for the past 19 years. His current research interests include pattern recognition, machine learning, soft computing, optimization, and big data. He received his M.Tech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published three research monographs and over 45 articles in international journals and conference proceedings.

“Readers interested in sentiment analysis research will find it useful. The research is a good contribution to our understanding of HGFRNNs and the development of a technique for sentiment analysis.” (Maulik A. Dave, Computing Reviews, January 25, 2021)

• Chapter1. Introduction.- Chapter 2. Current State of Art.- Chapter 3. Literature Review.- Chapter 4. Twitter Datasets Used.- Chapter 5. Visual and Text Sentiment Analysis.- Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks.- Chapter 7. Twitter Datasets Used.- Chapter 8. Experimental Results.- Chapter 9. Conclusion.

Arindam Chaudhuri is currently working as Principal Data Scientist at the Samsung R & D Institute in Delhi, India. He has worked in industry, research, and academics in the domain of machine learning for the past 19 years. His current research interests include pattern recognition, machine learning, soft computing, optimization, and big data. He received his M.Tech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published three research monographs and over 45 articles in international journals and conference proceedings.

“Readers interested in sentiment analysis research will find it useful. The research is a good contribution to our understanding of HGFRNNs and the development of a technique for sentiment analysis.” (Maulik A. Dave, Computing Reviews, January 25, 2021)

• Chapter1. Introduction.- Chapter 2. Current State of Art.- Chapter 3. Literature Review.- Chapter 4. Twitter Datasets Used.- Chapter 5. Visual and Text Sentiment Analysis.- Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks.- Chapter 7. Twitter Datasets Used.- Chapter 8. Experimental Results.- Chapter 9. Conclusion.

Arindam Chaudhuri is currently working as Principal Data Scientist at the Samsung R & D Institute in Delhi, India. He has worked in industry, research, and academics in the domain of machine learning for the past 19 years. His current research interests include pattern recognition, machine learning, soft computing, optimization, and big data. He received his M.Tech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published three research monographs and over 45 articles in international journals and conference proceedings.

“Readers interested in sentiment analysis research will find it useful. The research is a good contribution to our understanding of HGFRNNs and the development of a technique for sentiment analysis.” (Maulik A. Dave, Computing Reviews, January 25, 2021)

• Chapter1. Introduction.- Chapter 2. Current State of Art.- Chapter 3. Literature Review.- Chapter 4. Twitter Datasets Used.- Chapter 5. Visual and Text Sentiment Analysis.- Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks.- Chapter 7. Twitter Datasets Used.- Chapter 8. Experimental Results.- Chapter 9. Conclusion.